Solr/Lucene provides a lot of flexibility for adjusting relevancy scoring and improving search results. Roughly speaking there are two areas of concern: Firstly, a 'dynamic rank' calculation that is a function of the user query and document text fields. And secondly, a 'static rank' which is independent of the query and generally is a function of non-text document metadata. In this talk I will outline an easily understood, hand-tunable static rank system with a minimal number of parameters.

The obvious major feature of a search engine is to return results relevant to a user query. Perhaps less obvious is the huge role query independent document features play in achieving that. Google's PageRank is an example of a static ranking of web pages based on links and other secret sauce. In the Summon service, our 800 million documents have features like publication date, document type, citation count and Boolean features like the-article-is-peer-reviewed. These fields aren't textual and remain 'static' from query to query, but need to influence a document's relevancy score. In our search results, with all query related features being equal, we'd rather have more recent documents above older ones, Journals above Newspapers, and articles that are peer reviewed above those that are not. The static rank system I will describe achieves this and has the following features:

Query-time only calculation - nothing is baked into the index - with parameters adjustable at query time

The system is based on a signal metaphor where components are 'wired' together. System components allow multiplexing, amplifying, summing, tunable band-pass filtering, string-to-value-mapping all with a bare minimum of parameters.

An intuitive approach for mixing dynamic and static rank that is more effective than simple adding or multiplying.

A way of equating disparate static metadata types that leads to understandable results ordering.

Searching the library is complex. There's the catalog, article databases, journal title and database title look-ups, the library website, finding aids, knowledge bases, etc. How would users search if they could get to all of these resources from a single search box? I'll share what we've learned about single search at NCSU Libraries by tracking use of QuickSearch (http://www.lib.ncsu.edu/search/index.php?q=aerospace+engineering), our home-grown unified search application. As part of this talk I will suggest low-cost ways to collect real world use data that can be applied to improve search. I will try to convince you that data collection must be carefully planned and designed to be an effective tool to help you understand what your users are telling you through their behavior. I will talk about how the fragmented library resource environment challenges us to provide useful and understandable search environments. Finally, I will share findings from analyzing millions of user transactions about how people search the library from a production single search box at a large university library.

Digital library administrators are frequently asked questions like "How many times was that document downloaded", or "What’s the most popular book in our collection?" Conventional web logging software, such as AWStats, can only answer those questions some of the time, and there’s always the question of whether or not the data is polluted by non-users, such as spiders and crawlers. Google Analytics, (http://google.com/analytics/) , a JavaScript-based solution that excludes most crawlers and bots, shows how users found your site and how they explored it.

The presentation will review tracking search queries, adding events such as clicking external links or downloading files, and custom variables, to track user behavior that is normally difficult to track. We'll also discuss using jQuery scripts to add tracking code to the page without having to modify the underlying web application. Once you've collected data, you may use the Google Analytics API to extract data and integrate it with data from your digital repository to show granular data about individual items in your Digital Library. Finally, we'll discuss how this information allows you to improve the user experience, and summarize some of the research we are doing with our digital repository and the data gathered from Google Analytics.

The Grateful Dead Archive at the University of California (Santa Cruz) is a collection of over 600 linear feet of material, including: business records, photographs, posters, fan envelopes, tickets, video, audio (oral histories, interviews and music) and 3-d objects such as stage props and band merchandise. In addition, with the release of the Grateful Dead Archive Online website in 2012, the Archive will start actively collecting artifacts from an enthusiastic community of Grateful Dead fans.

This talk will discuss the challenges of merging a traditional archive with a socially constructed one. We will also present the first round of development and explain how we're using tools like Omeka, ContentDM, UC3 Merritt, djatoka, Kaltura, Google Maps, and Solr to lay the foundation for a robust and engaging site. Future directions, like the integration/development of better curation tools and what we hope to learn from opening the archive to contributions from a large community of fans, will also be discussed.

UI development is hard and too often ends up as an after-thought to computer programmers - if you were a CS major in college I'll bet you didn't have many, if any, design courses. I'll talk about how to involve the users upfront with design and some common pitfalls of this approach. I'll also make a case for why you should do the screen design before a single line of code is written. And I'll throw in some ideas for increasing usability and attractiveness of your web applications. I'd like to make a case study of the UI development of our open source ERMS.

"The code itself is unimportant; a project is only as useful as people actually find it." - Linus Torvalds[1]

Usability has been a buzzword for some time now, but what is the process for making the the transition toward a better user experience, and hence, better designed library sites? I will discuss the one facet of the process my team is using to redesign the Finding Aids site for Princeton University Libraries (still in development). The approach involves the use of rapid prototyping, with Bootstrap [2], to make sure we are on track with what users and stakeholders expect up front, and throughout the development process.

Because Bootstrap allows for early and iterative user feedback, it is more effective than the historic Photoshop mockups/wireframe technique. The Photoshop approach allows stakeholders to test the look, but not the feel -- and often leaves developers scratching their heads. Being a CSS/HTML/Javascript grid-based framework, Bootstrap makes it easy for anyone with a bit of HTML/CSS chops to quickly build slick, interactive prototypes right in the browser -- tangible solutions which can be shared, evaluated, revised, and followed by all stakeholders (see Minimum Viable Products [3]). Efficiency is multiplied because the customized prototypes can flow directly into production use, as is the goal with iterative development approaches, such as the Agile methodology.

While Bootstrap is not the only framework that offers grid-based layout, development is expedited and usability is enhanced by Bootstraps use of of "prefabbed" conventional UI patterns, clean typography, and lean Javascript for interactivity. Furthermore, out-of-the box Bootstrap comes in a fairly neutral palette, so focus remains on usability, and does not devolve into premature discussions of color or branding choices. Finally, using Less can be a powerful tool in conjunction with Bootstrap, but is not necessary. I will discuss the pros and cons, and offer examples for how to getting up and running with or without Less.

Users expect good design. This talk will delve into what makes really great design, what to look for, and how to do it. Learn the principles of great design to take your applications, user interfaces, and projects to a higher level. With years of experience in graphic design and illustration, Lisa will discuss design principles, trends, process, tools, and development. Design examples will be from her own projects as well as a variety from industry. You’ll walk away with design knowledge that you can apply immediately to a variety of applications and a number of top notch go-to resources to get you up and running.

HathiTrust Large-Scale search provides full-text search services over nearly 10 million full-text books using Solr for the back-end. Our index is around 5-6 TB in size and each shard contains over 3 billion unique terms due to content in over 400 languages and dirty OCR.

Searching the full-text of 10 million books often results in very large result sets. By conference time a number of features designed to help users narrow down large result sets and to do exploratory searching will either be in production or in preparation for release. There are often trade-offs between implementing desirable user features and keeping response time reasonable in addition to the traditional search trade-offs of precision versus recall.

What's the right metadata standard to use for a digital repository? There isn't just one standard that fits documents, videos, newspapers, audio files, local data, etc. And there is no standard to rule them all. So what do you do? At UC San Diego Libraries, we went down a conceptual level and attempted to hold every piece of metadata and give each holding place some context, hopefully in a common namespace. RDF has proven to be the ideal solution, and allows us to work with MODS, PREMIS, MIX, and just about anything else we've tried. It also opens up the potential for data re-use and authority control as other metadata owners start thinking about and expressing their data in the same way. I'll talk about our workflow which takes metadata from a stew of various sources (CSV dumps, spreadsheet data of varying richness, MARC data, and MODS data), normalizes them into METS by our Metadata Specialists who create an assembly plan, and then ingests them into our digital asset management system. The result is a beautiful graph of RDF triples with metadata poised to be expressed as HTML, RSS, METS, XML, and opens linked data possibilities that we are just starting to explore.